ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…As an outcome, the stack clas- sifier with XGBoost as the meta-classifier, trained with 65 important features determined by the Principal Component Analysis (PCA) technique, achieved the best performance among all the models. …”
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Enhancing PV power forecasting through feature selection and artificial neural networks: a case study by Mokhtar Ali, Abdelhalim Rabehi, Abdelkerim Souahlia, Mawloud Guermoui, Ali Teta, Imad Eddine Tibermacine, Abdelaziz Rabehi, Mohamed Benghanem, Takele Ferede Agajie

“…For instance, integrating ReliefF with MLP reduced the normalized mean absolute error (nMAE) to 9.21% with an R2 of 0.9608, while the best LSTM configuration achieved an nMAE of 9.29% and an R2 of 0.946 when using Chi-square selected features. These findings confirm that careful feature selection enhances model performance, reduces complexity, and ensures better generalization, offering valuable insights for more efficient solar energy management and grid stability.…”
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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…Results Seventy-eight features were extracted and reduced to ten features for model development. …”
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Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy by Nuo Yu, Yidong Wan, Lijing Zuo, Ying Cao, Dong Qu, Wenyang Liu, Lei Deng, Tao Zhang, Wenqing Wang, Jianyang Wang, Jima Lv, Zefen Xiao, Qinfu Feng, Zongmei Zhou, Nan Bi, Tianye Niu, Xin Wang

“…Based on MRI, CT, and the hybrid image data, three prediction models were built. The predictive performance of the radiomics models was evaluated in the training cohort and verified in the validation cohort using AUC values. …”
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The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network by Nurbaity Sabri, Haza Nuzly Abdull Hamed, Zaidah Ibrahim, Kamalnizat Ibrahim, Mohd Adham Isa, Norizan Mat Diah

“…Thus, an appropriate Features Extraction (FE) method is crucial to producing a good ML classification model. …”
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Sea Surface Floating Small-Target Detection Based on Dual-Feature Images and Improved MobileViT by Yang Liu, Hongyan Xing, Tianhao Hou

“…This study addresses the challenge of weak feature representation in one-dimensional (1D) sea clutter time-series analysis and suboptimal detection performance for sea surface small targets. …”
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TCCDNet: A Multimodal Pedestrian Detection Network Integrating Cross-Modal Complementarity with Deep Feature Fusion by Shipeng Han, Chaowen Chai, Min Hu, Yanni Wang, Teng Jiao, Jianqi Wang, Hao Lv

“…Specifically, the efficient multi-scale attention C2f (EMAC) is designed for the backbone, which combines the C2f structure with an efficient multi-scale attention mechanism to achieve feature weighting and fusion, thereby enhancing the model’s feature extraction capacity. …”
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SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning by Yanqing Wang, Xinru Zhan, Wanquan Liu, Yingying Li, Kexin Guo, Huafeng Wang

“…Although deep learning techniques, particularly CNNs, have offered promise for LDCT denoising, their inherent focus on local features and the scarcity of extensive training data can limit their performance and ability to generalize effectively. …”
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MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation by Tianen Ma, Xinwei Chen, Linlin Xu, Pengfei Ma, Peilin Yu

“…In the final stages, a refined multiscale feature fusion module was embedded in the decoder to strategically integrate the feature maps generated, thus iteratively merging layered features for enhanced segmentation. …”
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BiFFN: Bi-Frequency Guided Feature Fusion Network for Visible–Infrared Person Re-Identification by Xingyu Cao, Pengxin Ding, Jie Li, Mei Chen

“…These improvements verify that our model, with the integration of feature fusion and the incorporation of frequency domains, significantly reduces modality gaps and outperforms previous methods.…”
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Advanced feature engineering in Acute:Chronic Workload Ratio (ACWR) calculation for injury forecasting in elite soccer. by Jaime B Matas-Bustos, Antonio M Mora-García, Moisés de Hoyo Lora, Alejandro Nieto-Alarcón, Francisco T Gonzalez-Fernández

“…In this paper, we propose a novel feature engineering framework for training load management, inspired by bilinear modeling and signal processing principles. …”
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Comparative Analysis of Deep Learning-Based Feature Extraction and Traditional Classification Approaches for Tomato Disease Detection by Hakan Terzioğlu, Adem Gölcük, Adnan Mohammad Anwer Shakarji, Mateen Yilmaz Al-Bayati

“…Twenty-one deep learning models were evaluated, and the top five performers (EfficientNet-b0, NasNet-Large, ResNet-50, DenseNet-201, and Places365-GoogLeNet) were selected for feature extraction. …”
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MIF-YOLO: An Enhanced YOLO with Multi-Source Image Fusion for Autonomous Dead Chicken Detection by Jiapan Li, Yan Zhang, Yong Zhang, Hongwei Shi, Xianfang Song, Chao Peng

“…To address the challenge of feature extraction under conditions of significant occlusion, the model incorporates the Rep-DCNv3 module, which augments the backbone network's capacity to discern subtle characteristics of dead chickens. …”
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Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios by Angel Luis Perales Gomez, Lorenzo Fernandez Maimo, Alberto Huertas Celdran, Felix J. Garcia Clemente

“…These features will be used to train a new Deep Learning model that discriminates between adversarial and non-adversarial samples. …”
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Unpaired Spatio-Temporal Fusion for Remote Sensing Images via Deformable Global-Local Feature Alignment by Xinlan Ding, Huihui Song, Xu Zhang

“…Next, we perform feature fusion using the cross-communication mixture of experts module, which adaptively retains both local features and global representations. …”
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Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis by Huibo Zhang, Ming Luo, Junwei Feng, Juan Tan, Yan Jiang, Dmitrij Frishman, Yang Liu

“…Random forest models incorporating clinical/pathological data with (M1) and without (M2) TIL features (TIL scores and sTILCs) were developed on a training cohort (N = 312) to predict LNM, and performance was compared across validation (N = 78) and independent test cohorts (N = 148). …”
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Anomaly Detection in Industrial Machine Sounds Using High-Frequency Features and Gate Recurrent Unit Networks by Thi-Thu-Huong Le, Andro Aprila Adiputra, Jiwon Yun, Howon Kim

“…Experimental results demonstrate that eXtreme Gradient Boosting (XGBoost) outperforms Support Vector Machine (SVM) and Decision Tree (DT) models in the ML approach across both feature sets. …”
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A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…This study developed a lightweight model for recognizing peanut seed epidermal features. …”
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Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU by Ashish Kumar Singh, Anoj Kumar

“…Abstract High-dimensional datasets often pose challenges due to the presence of numerous irrelevant and redundant features, which can compromise the performance of machine learning models. …”
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Emotion Recognition Method Using U-Net Neural Network With Multichannel EEG Features and Differential Entropy Characteristics by Haitao Huang, Ying Deng, Bowen Hao, Wenguang Liu, Xiaoyu Tu, Guojun Zeng

“…Furthermore, to enhance the accuracy of emotion recognition for specific populations, the size of data blocks (patches) and model hyperparameters were dynamically adjusted. …”
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